This paper proposes a novel methodology based on neural architecture search to assess the usefulness of newly developed architecture layers and, in particular, morphological layers.
The following paper empirically investigates morphological layers applied to deep networks on CIFAR10/CIFAR100 [1]} and an edge detection task using BSD500 [2]}, to determine the optimal design for morphological layers. We compare our results to the best architecture discovered using conventional convolutional layers.
